Automated Arrhythmia Classification System: Proof-of-Concept With Lightweight Model on an Ultra-Edge Device

Arrhythmia can lead to severe complications and early detection of arrhythmia is crucial to prevent progression. Electrocardiograms are the most reliable measure for detecting arrhythmia. This study aims to implement a practical ultra-edge-computing system for automated arrhythmia classification, in...

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Bibliografische Detailangaben

Hauptverfasser:	Namho Kim, Seongjae Lee, Seungmin Kim, Sung-Min Park
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	IEEE 2024-01-01
Schriftenreihe:	IEEE Access
Schlagworte:	Edge computing knowledge distillation model compression on-device inference pruning
Online-Zugang:	https://ieeexplore.ieee.org/document/10704628/
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